Stock Market Trading and Market Conditions

This paper investigates the dynamic relation between market-wide trading activity and returns in 46 markets. Many stock markets exhibit a strong positive relation between turnover and past returns. These findings stand up in the face of various controls for volatility, alternative definitions for turnover, and differing sample periods, and are present at both the weekly and daily frequency. However, the magnitude of this relation varies widely across markets. Several competing explanations are examined by linking cross-country variables to the magnitude of the relation. The relation between returns and turnover is stronger in countries with restrictions on short sales and where stocks are highly cross-correlated; it is also stronger among individual investors than among foreign or institutional investors. In developed economies, turnover follows past returns more strongly in the 1980s than in the 1990s. The evidence is consistent with models of costly stock market participation in which investors infer that their participation is more advantageous following higher stock returns.

Daily Cross-Border Equity Flows: Pushed or Pulled?

In a model that is consistent with the existence of a home bias and with foreign investors that are less informed than domestic investors, we show that unexpectedly high worldwide returns lead to net equity inflows into small countries. In addition, a small country experiences net equity inflows when its stocks earn unexpectedly high returns. We investigate these predictions using daily data on net equity flows for nine emerging market countries and find that equity flows are positively related to host country stock returns as well as market performance abroad. Both our theoretical model and our empirical analysis show that global stock return performance is an important factor in understanding equity flows.

Investor Behavior in the Mutual Fund Industry: Evidence from Gross Flows

Using a large sample of monthly gross flows from 1997 to 2003, we uncover several previously undocumented regularities in investor behavior. First, investor purchases and sales produce fund-level gross flows that are highly persistent. Persistence in fund flows dominates performance as a predictor of future fund flows. More importantly, failing to account for flow persistence leads to incorrect inferences with respect to the relation between performance and flows. Second, we document that investors react differently to performance depending on the type of fund, and that investor trading activity produces meaningful differences in the persistence of fund flows across mutual fund types. Third, at least some investors appear to evaluate and respond to mutual fund performance over much shorter time spans than previously assessed. Additionally, we document differences in the speed and magnitude of investors’ purchase and sales responses to performance

Investors Do Respond to Poor Mutual Fund Performance: Evidence from Inflows and Outflows

Abstract We examine the relation between mutual fund performance and gross flows for a large sample of actively managed U.S. mutual funds. Unlike previous studies that have only examined periods of generally increasing net flows, our sample includes periods of both increasing and decreasing net flows. We find that outflows are related to performance, with investors withdrawing money from poor performers. We also find that outflows and inflows respond asymmetrically to performance, outflows increase more aggressively following poor performance, and inflows increase more aggressively following good performance. Additionally, we find a symmetric performance net flow relation

Forecasting Variance Swap Payoffs

We investigate the predictability of payoffs from selling variance swaps on the S&P500, US 10-year treasuries, gold, and crude oil. In-sample analysis shows that structural breaks are an important feature when modeling payoffs, and hence the ex post variance risk premium. Out-of-sample tests, on the other hand, reveal that structural break models do not improve forecast performance relative to simpler linear (or state invariant) models. We show that a host of variables that had previously been shown to forecast excess returns for the four asset classes, contain predictive power for ex post realizations of the respective variance risk premia as well. We also find that models fit directly to payoffs perform as well or better than models that combine the current variance swap rate with a realized variance forecast. These novel findings have important implications for variance swap sellers, and investors seeking to include volatility as an asset in their portfolio

Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments

We conduct a comprehensive out-of-sample assessment of the economic value adding of commodities in multiasset investment strategies that exploit the predictability of asset return moments. We find that predictability makes the inclusion of commodities profitable even when short selling and high leverage are not permitted. For instance, a mean-variance (non-mean-variance) investor with moderate risk aversion and leverage, rebalancing quarterly, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities to her stock, bond, and cash portfolio. Previous research had reached mixed or even opposite conclusions, especially in an out-of-sample context

Analysis of High Dimensional Multivariate Stochastic Volatility Models.

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate stochastic volatility model. A unified analysis of the model, and its special cases, is developed that encompasses estimation, filtering and model choice. The centerpieces of the estimation algorithm (which relies on MCMC methods) are: (1) a reduced blocking scheme for sampling the free elements of the loading matrix and the factors and (2) a special method for sampling the parameters of the univariate SV process. The resulting algorithm is scalable in terms of series and factors and simulation-efficient. Methods for estimating the log-likelihood function and the filtered values of the time-varying volatilities and correlations are also provided. The performance and effectiveness of the inferential methods are extensively tested using simulated data where models up to 50 dimensions and 688 parameters are fit and studied. The performance of our model, in relation to various multivariate GARCH models, is also evaluated using a real data set of weekly returns on a set of 10 international stock indices. We consider the performance along two dimensions: the ability to correctly estimate the conditional covariance matrix of future returns and the unconditional and conditional coverage of the 5% and 1% value-at-risk (VaR) measures of four pre-defined portfolios